Correction to: Brain and Human Body Modeling 2020

A chapter was removed from this Open Access volume and three subsequent chapters were moved into sections of the book which was not appropriate. The TOC has now been updated to match the correct order.

Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons

In this study, the boundary element fast multipole method or BEM-FMM is applied to model compact clusters of tightly spaced pyramidal neocortical neurons firing simultaneously and coupled with a high-resolution macroscopic head model. The algorithm is capable of processing a very large number of surface-based unknowns along with a virtually unlimited number of elementary microscopic current dipole sources distributed within the neuronal arbor.The realistic cluster size may be as large as 10,000 individual neurons, while the overall computation times do not exceed several minutes on a standard server. Using this approach, we attempt to establish how well the conventional lumped-dipole model used in electroencephalography/magnetoencephalography (EEG/MEG) analysis approximates a compact cluster of realistic neurons situated either in a gyrus (EEG response dominance) or in a sulcus (MEG response dominance).

Profiling General-Purpose Fast Multipole Method (FMM) Using Human Head Topology

In this study, we characterize the performance of the fast multipole method (FMM) in solving the Laplace and Helmholtz equations. We use the FMM library developed by the group of Dr. L. Greengard. This version of the FMM algorithm is multilayer with no priori limit on the number of levels of the FMM tree, although, after about thirty levels, there may be floating point issues. A collection of high-resolution human head models is used as test objects. We perform a detailed analysis of the runtime and memory consumption of the FMM in a wide range of frequencies, problem sizes, and precisions required. Although we focus on two-manifold test cases, the results are generalizable to other topologies as well. The tests are conducted on both Windows and Linux platforms. The results obtained in this study can serve as a general benchmark for the performance of FMM. It can also be employed to pre-estimate the efficiency of numerical modeling methods (e.g., the boundary element method) accelerated by FMM.

Estimating Electric Field and SAR in Tissue in the Proximity of RF Coils

Medical implants that require recharging typically use magnetic resonant coupling of transmit (external) and receive (internal) RF coils. Apart from magnetic field, the transmit coil creates a time-varying electric field that excites currents not only in the receive coil but also in the surrounding tissues. Radio frequency (RF) exposure assessment for inductive systems used in wireless powering and telemetry is done using electric field, specific absorption rate (SAR), and induced current as metrics. Full-wave analysis using RF simulation tools such as Ansys HFSS is generally used to estimate these metrics, and the results are widely accepted. However, such simulation-based analysis is quite rigorous and time-consuming, let alone the complexities with setting up the simulation.In this paper, we present a simple approach to estimating exposure (electric field, SAR, induced current) from fundamental electromagnetic principles enabling ability to arrive at results quickly. It significantly reduces the computational time in iterative approaches where multiple simulation runs are needed.

How Do Tumor-Treating Fields Work?

Since approved by the FDA for the treatment of glioblastoma brain cancer in 2015, tumor-treating fields (TTFields) have rapidly become the fourth modality to treat cancer, along with surgery, chemotherapy, and radiation [1]. TTFields are now in clinical trials for a variety of cancer types. While efficacy has been proven in the clinic, the higher efficacy is demonstrated in vitro and in animal models, which indicates much greater clinical efficacy is possible. To attain the great promise of TTFields, uncovering the mechanisms of action (MoA) is necessary.

Modelling Studies of Non-invasive Electric and Magnetic Stimulation of the Spinal Cord

Experimental studies on transcutaneous spinal cord direct current and magnetic stimulation (tsDCS and tsMS, respectively) show promising results in the neuromodulation of spinal sensory and motor pathways, with possible clinical application in spinal functional rehabilitation. Modelling studies on the electric field (EF) distribution during tsDCS and tsMS can be powerful tools to understand the underlying biophysics and to guide stimulation protocols for a specific clinical target. In this chapter, we review modelling studies of tsDCS and report on our own modelling findings on tsDCS and tsMS. We discuss the main differences between the EF induced by these two stimulation techniques and the implications for clinical practice, addressing the relevance of modelling studies for more personalized target protocols and individualized dosing.

Tumor-Treating Fields at EMBC 2019: A Roadmap to Developing a Framework for TTFields Dosimetry and Treatment Planning

Tumor Treating Fields (TTFields) are electric fields known to exert an anti-mitotic effect on cancerous tumors. TTFields have been approved for the treatment of glioblastoma and malignant pleural mesothelioma. Recent studies have shown a correlation between TTFields doses delivered to the tumor bed and patient survival. These findings suggest that patient outcome could be significantly improved with rigorous treatment planning, in which numerical simulations are used to plan treatment in order to optimize delivery of TTFields to the tumor bed.Performing such adaptive planning in a practical and meaningful manner requires a rigorous and scientifically proven framework defining TTFields dose and showing how dose distribution influences disease progression in different malignancies (TTFields dosimetry). At EMBC 2019, several talks discussing key components related to TTFields dosimetry and treatment planning were presented. Here we provide a short overview of this work and discuss how it sets the foundations for the emerging field of TTFields dosimetry and treatment planning.

Computational Models of Brain Stimulation with Tractography Analysis

Computational human head models have been used in studies of brain stimulation. These models have been able to provide useful information that can’t be acquired or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. We hereby combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogram with 2000 neural fibres from diffusion-weighted magnetic resonance scans and extracted the information on electrical potential from the FE ECT model of the same head. Two different electrode placements and three different white matter conductivity settings were simulated and compared. We calculated the electric field and second spatial derivatives of the electrical potential along the fibre direction, which describes the activating function for homogenous axons, and investigated sensitive regions of white matter activation. Models with anisotropic white matter conductivity yielded the most distinctive electric field and activating function distribution. Activation was most likely to appear in regions between the electrodes where the electric potential gradient is most pronounced.

A Computational Parcellated Brain Model for Electric Field Analysis in Transcranial Direct Current Stimulation

Recent years have seen the use of increasingly realistic electric field (EF) models to further our knowledge of the bioelectric basis of noninvasive brain techniques such as transcranial direct current stimulation (tDCS). Such models predict a poor spatial resolution of tDCS, showing a non-focal EF distribution with similar or even higher magnitude values far from the presumed targeted regions, thus bringing into doubt the classical criteria for electrode positioning. In addition to magnitude, the orientation of the EF over selected neural targets is thought to play a key role in the neuromodulation response. This chapter offers a summary of recent works which have studied the effect of simulated EF magnitude and orientation in tDCS, as well as providing new results derived from an anatomically representative parcellated brain model based on finite element method (FEM). The results include estimates of mean and peak tangential and normal EF values over different cortical regions and for various electrode montages typically used in clinical applications.

Improving Tumor-Treating Fields with Skull Remodeling Surgery, Surgery Planning, and Treatment Evaluation with Finite Element Methods

Tumor-treating fields (TTFields) are alternating fields (200 kHz) used to treat glioblastoma (GBM), which is one of the deadliest cancer diseases of all. Glioblastoma is a type of malignant brain cancer, which causes significant neurological deterioration and reduced quality of life, and for which there is currently no curative treatment. TTFields were recently introduced as a novel treatment modality in addition to surgery, radiation therapy, and chemotherapy. The fields are induced noninvasively using two pairs of electrode arrays placed on the scalp. Due to low electrical conductivity, significant currents are shielded from the intracranial space, potentially compromising treatment efficacy. Recently, skull remodeling surgery (SR-surgery) was proposed to address this issue. SR-surgery comprises the formation of skull defects or thinning of the skull over the tumor to redirect currents toward the pathology and focally enhance the field intensity. Safety and feasibility of this concept were validated in a clinical phase 1 trial (OptimalTTF-1), which also indicated promising survival benefits. This chapter describes the FE methods used in the OptimalTTF-1 trial to plan SR-surgery and assess treatment efficacy. We will not present detailed modeling results from the trial but rather general concepts of model development and field calculations. Readers are kindly referred to Wenger et al. [1] for a more general overview of the clinical implications and applications of TTFields modeling.